The present invention is directed generally towards a digital image processing system and method for estimating the frequency dependence and grey-level dependence of noise introduced by an image source device and, more particularly, for generating a spatial device profile describing the noise introduced by the source device.
Here, an input "device" may be a piece of hardware such as a scanner or camera, but may also be any abstract input device: a processing module of an image processing system that receives an image, transforms it in some way with the possible generation or propagation of noise, and outputs it. Any such abstract device has color characteristics--point transformations of input values (say, rgb triplets) occurring in the device--as well as spatial characteristics--device transformations where each value in the output image may depend on a neighborhood of nearby points in the input image.
Device color characteristics
One common technique for associating color characteristic information with a particular device is through the use of a color profile. A device color profile is standardized and defined as "a digital representation of the relation between device coordinates and a device-independent specification of color" in the International Color Consortium (ICC) Profile Format Specification, Version 3.3, Nov. 11, 1996, page 101 incorporated by reference herein in its entirety for supplemental background information which is non-essential but helpful in appreciating the applications of the present invention.
The ICC defines five major classes of color profile: device profile, device-link profile, color space conversion profile, abstract profile and named color profile. Device profiles are further broken down into subclasses of input profiles, output profiles and display profiles. The ICC profile is a tagged file structure which includes three main sections: a header, a tag table and tagged element data. From the profile header, a color management system (CMS) obtains and operates on general device information, file information and profile information.
Device spatial characteristics
Spatial characteristics of the elements of an image processing system can be represented by spatial characteristic functions describing noise and image signal transform characteristics of the device under consideration. In practice these image signal transform characteristics are represented by mid-tone Wiener Noise Spectra (WNS), small signal modulation transfer functions (MTF) measured in mid-tone grey-levels, and LUTS describing how noise variance changes as a function of grey-level. The WNS can be represented as a one-dimensional vector, describing how noise power changes as a function of spatial frequency in one dimension, or as a two-dimensional matrix, describing how noise power changes as a function of spatial frequency in two dimensions. A two-dimensional noise power matrix may be referred to as a "noise mask" in this application. U.S. patent application Ser. No. 08/440,639 filed May 15, 1995 for noise reduction using a Wiener variant filter in a pyramid image is hereby incorporated by reference in its entirety to provide supplemental background information which is non-essential but helpful in appreciating the applications of the present invention.
The inclusion of non-color, spatial information into profiles is disclosed in U.S. Patent Application Ser. No. 08/709,487 filed Sep. 6, 1996 by Hultgren et al. and incorporated by reference herein in its entirety for background information. Spatial information can be represented, for instance, by private spatial tags under the ICC recommendations. The spatial tags should include information as to which class the particular characteristic function belongs, i.e. modulation transfer functions, one-dimensional noise power spectra, two-dimensional noise power spectra (referred to in this application as "noise masks"), or one-dimensional noise variance grey-level dependency LUTs. In the case of scanners, a tag should be included to designate the number of dots per inch (DPI) scanned during image acquisition. The tagged format should also include information sufficient to identify both the relevant units of spatial frequency and the dimensionality of the characteristic functions. Propagation of characteristic functions is calculated within the context of a model based image processing system.
The spatial tags of interest for the present invention include one-dimensional noise variance LUTs describing grey-level dependency of noise, and two-dimensional noise masks, describing the two-dimensional frequency dependency of the noise. These characteristic functions, and their formatted counterparts as spatial tags in a spatial device profile, can be used as part of a noise reduction image processing system, as for example described in the aforementioned U.S. patent application Ser. No. 08/440,639, and in U.S. patent application Ser. No. 08/966,140 filed Nov. 7, 1997 by Wober et al., both incorporated by reference in their entirety for supplemental background information which is non-essential but helpful in appreciating the applications of the present invention.
Application Ser. No. '639 discloses a process and system for removing noise from an image represented by an image signal by first noise modeling an image signal source to generate both noise masks and lookup table values characteristic of noise within each channel, and then applying the noise masks and LUT values to the image signal for noise removal. The image is first captured as an electronic image signal by the image signal source, then represented by a pyramid image representation whereby each successive level of the pyramid is constructed from direct current (DC) values of the previous level, and each level of the pyramid corresponds to a different frequency band of the image signal. A Wiener variant filter using DCT transforms is used to filter DCT coefficients at each level. The image is restored with reduced noise by replacing DC values with next level inverse discrete cosine transform (IDCT) coefficients, then performing an IDCT on the results.
Application Ser. No. '140 discloses a method and system for structuring an image which corresponds to an original array of pixels, as a forward discrete even cosine transform pyramid having a predetermined number of levels where each level is associated with a different DCT frequency band, includes, respectively, the steps or functionality of: partitioning the original array into blocks of overlapped pixels; taking a DCT of each block of overlapped pixels to generate blocks of first level DCT coefficients forming a first level of the DCT pyramid; storing the first level of the DCT pyramid; selecting and storing at least one of the first level DCT coefficients of each block of first level DCT coefficients into a first temporary array; partitioning the first temporary array into blocks of overlapped coefficients; and taking a DCT of each block of overlapped coefficients of the first temporary array to generate blocks of second level DCT coefficients forming a second level of the DCT pyramid. Additional levels can be created by repeating the previous steps.
At times an image can be acquired by a digital processing system but no device profile describing the spatial characteristics of the device is available. When the source device through which the image has passed is unknown, or no device profile is available, then accurate reproduction of the image to a destination device, such as a printer, is uncertain. Values must be estimated for the unknown or incomplete spatial information so that the reproduced image will vary as little as possible from the original image.